Fig. REF  shows the high-level architectural overview of ORE. We choose Faster R-CNN [1]} as the base detector as Dhamija . [2]} has found that it has better open set performance when compared against one-stage Retina-Net detector [3]} and objectness based YOLO detector [4]}.
Faster R-CNN [1]} is a two stage object detector. In the first stage, a class-agnostic Region Proposal Network (RPN) proposes potential regions which might have an object from the feature maps coming from a shared backbone network. The second stage classifies and adjusts the bounding box coordinates of each of the proposed region.
The features that are generated by the residual block in the Region of Interest (RoI) head are contrastively clustered.
The RPN and the classification head is adapted to auto-label and identify unknowns respectively. We explain each of these coherent constituent components, in the following subsections:
